Comparison with State-of-the-Art (SOTA):
In Table REF , \(\text{HDCAM}\)  is compared with recent SOTA recurrent (Dilated LSTM) [1]}, convolutional (CNN), hybrid LSTM-CNN [2]}, and hybrid attention-CNN [3]} models on Ninapro DB2 dataset [4]}. Overall, our model demonstrates better accuracy versus the number of parameters compared to other methods. As shown in Table REF , for the window size of 200 ms, all variants of the proposed model outperform other SOTA approaches. For instance, our \(\text{XXSmall}\)  model has \(53.3\)  times less parameter than Dilated LSTM, but obtains a \(2.1\%\)  gain in the top-1 accuracy. Compared to the best performing TC-HGR model (Model 4), our \(\text{XXSmall}\)  and \(\text{Small}\)  models improve the accuracy for \(0.38\%\)  and \(1.56\%\)  with \(4.59\)  and \(1.62\)  times less number of parameters, respectively. Moreover, as shown in Table REF , for the window size of 300 ms, \(\text{XSmall}\)  and \(\text{Small}\)  variants of \(\text{HDCAM}\)  obtain \(82.61\%\)  and \(82.91\%\)  top-1 accuracy respectively, both surpassing all previous SOTA methods with fewer number of parameters (see Fig. REF ). Dilation-based LSTM [1]}, as the previous SOTA model on DB2 dataset, reached to \(82.4\%\)  top-1 accuracy with \(1,102,801\)  number of parameters, while our \(\text{XSmall}\)  model attains better accuracy (\(82.61\%\) ) with only \(40,281\)  parameters, i.e., \(27.38\)  times fewer. It is worth noting that our \(\text{Small}\)  model achieved \(82.91\%\)  top-1 accuracy with \(58,441\)  parameters, reaching a new SOTA performance that demonstrates the effectiveness and the generalization of our design.
